Predicting translation behaviorsby using Hidden Markov Model

The translation process can be studied as sequences of activity units. The application of machine learning technology offers researchers new possibilities in the study of the translation process. This research project developed a program, activity unit predictor, using the Hidden Markov Model. The program takes in duration, translation phase, target language and fixation as the input and produces an activity unit type as the output. The highest prediction accuracy reached is 61%. As one of the first endeavors, the program demonstrates strong potential of applying machine learning in translation process research.